






"""simple docstring"""





import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE   :							Optional[int]          =		get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
  from transformers.models.mbart.modeling_mbart import shift_tokens_right

_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		25_0004
_SCREAMING_SNAKE_CASE   :							Any          =		25_0020






@require_sentencepiece
@require_tokenizers
class 	a		(   __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Any		=					MBartTokenizer
    SCREAMING_SNAKE_CASE		:	Tuple		=					MBartTokenizerFast
    SCREAMING_SNAKE_CASE		:	int		=					True
    SCREAMING_SNAKE_CASE		:	List[Any]		=					True






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Union[str, Any]:
      super().setUp()

      # We have a SentencePiece fixture for testing
      lowerCamelCase_       	=		MBartTokenizer(__SCREAMING_SNAKE_CASE			,		keep_accents=__SCREAMING_SNAKE_CASE   )
      tokenizer.save_pretrained(self.tmpdirname   )






    def   UpperCamelCase			(			self	:				List[str]   )	->						List[str]:
      lowerCamelCase_       	=		MBartTokenizer(__SCREAMING_SNAKE_CASE			,		keep_accents=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.tokenize('This is a test'   )
      self.assertListEqual(__SCREAMING_SNAKE_CASE			,		['▁This', '▁is', '▁a', '▁t', 'est']   )

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE   )			,		[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]			,		)

      lowerCamelCase_       	=		tokenizer.tokenize('I was born in 92000, and this is falsé.'   )
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE			,		[
              SPIECE_UNDERLINE + 'I',
              SPIECE_UNDERLINE + 'was',
              SPIECE_UNDERLINE + 'b',
              'or',
              'n',
              SPIECE_UNDERLINE + 'in',
              SPIECE_UNDERLINE + '',
              '9',
              '2',
              '0',
              '0',
              '0',
              ',',
              SPIECE_UNDERLINE + 'and',
              SPIECE_UNDERLINE + 'this',
              SPIECE_UNDERLINE + 'is',
              SPIECE_UNDERLINE + 'f',
              'al',
              's',
              'é',
              '.',
          ]			,		)
      lowerCamelCase_       	=		tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE   )
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE			,		[
              value + tokenizer.fairseq_offset
              for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
              #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
          ]			,		)

      lowerCamelCase_       	=		tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE   )
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE			,		[
              SPIECE_UNDERLINE + 'I',
              SPIECE_UNDERLINE + 'was',
              SPIECE_UNDERLINE + 'b',
              'or',
              'n',
              SPIECE_UNDERLINE + 'in',
              SPIECE_UNDERLINE + '',
              '<unk>',
              '2',
              '0',
              '0',
              '0',
              ',',
              SPIECE_UNDERLINE + 'and',
              SPIECE_UNDERLINE + 'this',
              SPIECE_UNDERLINE + 'is',
              SPIECE_UNDERLINE + 'f',
              'al',
              's',
              '<unk>',
              '.',
          ]			,		)






    def   UpperCamelCase			(			self	:				List[str]   )	->						List[Any]:
      if not self.test_slow_tokenizer:
        # as we don't have a slow version, we can't compare the outputs between slow and fast versions
        return

      lowerCamelCase_       	=		(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
          lowerCamelCase_       	=		self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )
          lowerCamelCase_       	=		self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )

          lowerCamelCase_       	=		tempfile.mkdtemp()

          lowerCamelCase_       	=		tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE   )
          lowerCamelCase_       	=		tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE   )

          # Checks it save with the same files + the tokenizer.json file for the fast one
          self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files   )   )
          lowerCamelCase_       	=		tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f   )
          self.assertSequenceEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

          # Checks everything loads correctly in the same way
          lowerCamelCase_       	=		tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE   )
          lowerCamelCase_       	=		tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE   )

          # Check special tokens are set accordingly on Rust and Python
          for key in tokenizer_pp.special_tokens_map:
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )   )
            # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
            # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

          shutil.rmtree(__SCREAMING_SNAKE_CASE   )

          # Save tokenizer rust, legacy_format=True
          lowerCamelCase_       	=		tempfile.mkdtemp()

          lowerCamelCase_       	=		tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE			,		legacy_format=__SCREAMING_SNAKE_CASE   )
          lowerCamelCase_       	=		tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE   )

          # Checks it save with the same files
          self.assertSequenceEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

          # Checks everything loads correctly in the same way
          lowerCamelCase_       	=		tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE   )
          lowerCamelCase_       	=		tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE   )

          # Check special tokens are set accordingly on Rust and Python
          for key in tokenizer_pp.special_tokens_map:
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )   )

          shutil.rmtree(__SCREAMING_SNAKE_CASE   )

          # Save tokenizer rust, legacy_format=False
          lowerCamelCase_       	=		tempfile.mkdtemp()

          lowerCamelCase_       	=		tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE			,		legacy_format=__SCREAMING_SNAKE_CASE   )
          lowerCamelCase_       	=		tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE   )

          # Checks it saved the tokenizer.json file
          self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files   )   )

          # Checks everything loads correctly in the same way
          lowerCamelCase_       	=		tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE   )
          lowerCamelCase_       	=		tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE   )

          # Check special tokens are set accordingly on Rust and Python
          for key in tokenizer_pp.special_tokens_map:
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )   )

          shutil.rmtree(__SCREAMING_SNAKE_CASE   )






@require_torch
@require_sentencepiece
@require_tokenizers
class 	a		(   unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					"""facebook/mbart-large-en-ro"""
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					[
        """ UN Chief Says There Is No Military Solution in Syria""",
        """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
    ]
    SCREAMING_SNAKE_CASE		:	List[Any]		=					[
        """Şeful ONU declară că nu există o soluţie militară în Siria""",
        """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
        """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
        """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
    ]
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					[8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]






    @classmethod
    def   UpperCamelCase			(			cls	:				int   )	->						int:
      lowerCamelCase_       	=		MBartTokenizer.from_pretrained(
          cls.checkpoint_name			,		src_lang='en_XX'			,		tgt_lang='ro_RO'   )
      lowerCamelCase_       	=		1
      return cls






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[int]:
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']			,		250001   )
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']			,		250004   )
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']			,		250020   )






    def   UpperCamelCase			(			self	:				List[Any]   )	->						List[Any]:
      lowerCamelCase_       	=		self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
      self.assertListEqual(self.expected_src_tokens			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				List[str]   )	->						List[str]:
      self.assertIn(__SCREAMING_SNAKE_CASE			,		self.tokenizer.all_special_ids   )
      lowerCamelCase_       	=		[RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
      lowerCamelCase_       	=		self.tokenizer.decode(__SCREAMING_SNAKE_CASE			,		skip_special_tokens=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		self.tokenizer.decode(generated_ids[1:]			,		skip_special_tokens=__SCREAMING_SNAKE_CASE   )
      self.assertEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      self.assertNotIn(self.tokenizer.eos_token			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Optional[int]:
      lowerCamelCase_       	=		['this is gunna be a long sentence ' * 20]
      assert isinstance(src_text[0]			,		__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		10
      lowerCamelCase_       	=		self.tokenizer(__SCREAMING_SNAKE_CASE			,		max_length=__SCREAMING_SNAKE_CASE			,		truncation=__SCREAMING_SNAKE_CASE   ).input_ids[0]
      self.assertEqual(ids[-2]			,		2   )
      self.assertEqual(ids[-1]			,		__SCREAMING_SNAKE_CASE   )
      self.assertEqual(len(__SCREAMING_SNAKE_CASE   )			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Dict   )	->						Union[str, Any]:
      self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']   )			,		[250026, 250001]   )






    def   UpperCamelCase			(			self	:				Dict   )	->						Any:
      lowerCamelCase_       	=		tempfile.mkdtemp()
      lowerCamelCase_       	=		self.tokenizer.fairseq_tokens_to_ids
      self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		MBartTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )
      self.assertDictEqual(new_tok.fairseq_tokens_to_ids			,		__SCREAMING_SNAKE_CASE   )






    @require_torch
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						List[Any]:
      lowerCamelCase_       	=		self.tokenizer(self.src_text			,		text_target=self.tgt_text			,		padding=__SCREAMING_SNAKE_CASE			,		return_tensors='pt'   )
      lowerCamelCase_       	=		shift_tokens_right(batch['labels']			,		self.tokenizer.pad_token_id   )

      # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
      assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
      assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
      assert batch.decoder_input_ids[1][-1] == 2
      assert batch.labels[1][-2:].tolist() == [2, RO_CODE]






    @require_torch
    def   UpperCamelCase			(			self	:				Dict   )	->						Optional[int]:
      lowerCamelCase_       	=		self.tokenizer(
          self.src_text			,		text_target=self.tgt_text			,		padding=__SCREAMING_SNAKE_CASE			,		truncation=__SCREAMING_SNAKE_CASE			,		max_length=len(self.expected_src_tokens   )			,		return_tensors='pt'			,		)

      lowerCamelCase_       	=		shift_tokens_right(batch['labels']			,		self.tokenizer.pad_token_id   )

      self.assertIsInstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      self.assertEqual((2, 14)			,		batch.input_ids.shape   )
      self.assertEqual((2, 14)			,		batch.attention_mask.shape   )
      lowerCamelCase_       	=		batch.input_ids.tolist()[0]
      self.assertListEqual(self.expected_src_tokens			,		__SCREAMING_SNAKE_CASE   )
      self.assertEqual(2			,		batch.decoder_input_ids[0, -1]   )  # EOS
      # Test that special tokens are reset
      self.assertEqual(self.tokenizer.prefix_tokens			,		[]   )
      self.assertEqual(self.tokenizer.suffix_tokens			,		[self.tokenizer.eos_token_id, EN_CODE]   )






    def   UpperCamelCase			(			self	:				List[str]   )	->						Any:
      lowerCamelCase_       	=		self.tokenizer(self.src_text			,		padding=__SCREAMING_SNAKE_CASE			,		truncation=__SCREAMING_SNAKE_CASE			,		max_length=3			,		return_tensors='pt'   )
      lowerCamelCase_       	=		self.tokenizer(
          text_target=self.tgt_text			,		padding=__SCREAMING_SNAKE_CASE			,		truncation=__SCREAMING_SNAKE_CASE			,		max_length=10			,		return_tensors='pt'   )
      lowerCamelCase_       	=		targets['input_ids']
      lowerCamelCase_       	=		shift_tokens_right(__SCREAMING_SNAKE_CASE			,		self.tokenizer.pad_token_id   )

      self.assertEqual(batch.input_ids.shape[1]			,		3   )
      self.assertEqual(batch.decoder_input_ids.shape[1]			,		10   )






    @require_torch
    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Union[str, Any]:
      lowerCamelCase_       	=		self.tokenizer._build_translation_inputs(
          'A test'			,		return_tensors='pt'			,		src_lang='en_XX'			,		tgt_lang='ar_AR'   )

      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE   )			,		{
              # A, test, EOS, en_XX
              'input_ids': [[62, 3034, 2, 250004]],
              'attention_mask': [[1, 1, 1, 1]],
              # ar_AR
              'forced_bos_token_id': 250001,
          }			,		)












"""simple docstring"""





from scipy.stats import pearsonr

import datasets


_SCREAMING_SNAKE_CASE   :							List[str]          =		'''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
'''


_SCREAMING_SNAKE_CASE   :							int          =		'''
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        [\'p-value\', \'pearsonr\']
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74
        >>> print(round(results[\'p-value\'], 2))
        0.15
'''


_SCREAMING_SNAKE_CASE   :							Dict          =		'''
@article{2020SciPy-NMeth,
author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
      Haberland, Matt and Reddy, Tyler and Cournapeau, David and
      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
      Kern, Robert and Larson, Eric and Carey, C J and
      Polat, Ilhan and Feng, Yu and Moore, Eric W. and
      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
      Harris, Charles R. and Archibald, Anne M. and
      Ribeiro, Antonio H. and Pedregosa, Fabian and
      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
      Computing in Python}},
journal = {Nature Methods},
year    = {2020},
volume  = {17},
pages   = {261--272},
adsurl  = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    , _KWARGS_DESCRIPTION						)
class 	a		(   datasets.Metric						):






    def   UpperCamelCase			(			self	:				Dict   )	->						Any:
      return datasets.MetricInfo(
          description=_DESCRIPTION			,		citation=_CITATION			,		inputs_description=_KWARGS_DESCRIPTION			,		features=datasets.Features(
              {
                  'predictions': datasets.Value('float'   ),
                  'references': datasets.Value('float'   ),
              }   )			,		reference_urls=['https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html']			,		)






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				Tuple=False   )	->						Tuple:
      if return_pvalue:
        lowerCamelCase_       	=		pearsonr(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
        return {"pearsonr": results[0], "p-value": results[1]}
      else:
        return {"pearsonr": float(pearsonr(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )[0]   )}




